We’ll start defining some important concepts and attributes of machine learning systems, things that need to be understood in order to start coding a system. As soon as you finish reading this article, you’ll have a notion of why would you use an ML solution and what do you need to build it.

Defining Machine Learning

Machine Learning is the science of programming computers so they can learn from data. An ML-based would process raw data and transform it into training instances which are part of the training set.

Training set: a set of multiple training instances used by the system to learn autonomously, algorithmic-based.

This is an example of a training set. Each line is a training instance. Note that the “Purchased” field is still unprocessed – your computer understands 0’s and 1’s, and not Yes’s and No’s

From the example above:

Country, Age, Salary and Purchased are data types or also attributes

A feature is usually an attribute plus its value (“Country” = “France”)

ML/AI systems vs. Mechanical Systems

Spam filters were one of the first practical and mainstream uses of machine learning, and they illustrate well such difference. A spam filter usually analyzes words within the email itself looking for red flags.

If you’re building a mechanical spam filter, you would have to hardcode all spam red flags. As it might be effective, it’s not so efficient since spam strategies are constantly changing.

What I’m saying is that a lot of human effort would be required to keep such a mechanical system up to date. In an ML scenario, the system would learn incrementally by itself by being fed training data (online learning, preferably).

Training what we can’t (or don’t want to) code

Coding a speech recognizer or a personal assistant like Siri or Alexa became possible thanks to machine learning. Well, they could be coded with no ML traces, but that’s the kind of work that becomes unnecessary when you have the powerful tools of ML.

Imagine if you had to hardcode all possible variations of each word and assign all of them to the corresponding letters… a huge chunk of work. Writing an algorithm that learns by itself is a better idea, given many examples for each word.

We can now conclude that ML and AI open uncountable possibilities for innovations, since their building time becomes shorter.

So, when to use machine learning?

Dynamic environment (ML can adapt to new data using online/batch learning)

Getting intel about complex problems and large amounts of data

Complex problems that are not so easy to code or would require a lot of human hours

Huge amounts of data and no known or developed algorithms

Machine Learning Systems

There are three ways to generally classify machine learning systems or algorithms:

Whether they are trained or not under human supervision (supervised, unsupervised, semisupervised or Reinforcement Learning)

Whether they can learn incrementally while running (online or batch learning)

Whether they compare new data points to known data points, or detect patterns in the new training data and build predictive models (instance based or model-based learning)

How systems are trained

Supervised Learning

The training data fed to the algorithms includes the desired solutions, called labels. Therefore, every training instance will contain a label.

Classification and Regression are typical supervised learning tasks:

Classification will set instances into different groups

Regression (or prediction) will predict values or actions by learning from predictors and their labels

Most important supervised learning algorithms:

Neural Networks (which can also be unsupervised)

Decision Trees

Random Forests

Linear Regression

Logistic Regression

Unsupervised Learning

The training data is unlabeled. The system learns by itself through data interpretation. Therefore, an unlabeled training set. There are three general uses for unsupervised learning:

Clustering

Association rule learning

Visualization and dimensionality reduction

Clustering will divide instances into clusters – which are groups that share traits in common.

Dimensionality reduction has the goal of simplifying the data without losing too much information. For instance, the price of a house might be correlated with its location so the dimensionality reduction algorithm will merge them into one feature. Feature extraction is the name of this technique. This helps performance in a very considerable way.

Anomaly detection is also a task for unsupervised learning, like credit card fraud detection.

Semisupervised Learning

Combination of both supervised and unsupervised algorithms – a portion of the data is labeled, but the other is not. Usually, the system will identify patterns or will cluster the data and then the programmer needs to insert labels to each pattern or cluster.

Deep Belief Networks (DBNs) are based on Restricted Boltzmann Machine (RBM), which is an unsupervised learning component. RBMs are trained through unsupervised learning, and then the system is fine-tuned using supervised learning techniques (insertion of labels).

Facial recognition is a good example of semisupervised learning: the system by itself will identify that the person is there and, depending on the system, will also identify their physical attributes (hair and eye color, skin tone, shapes…), and then the instance will be fed with the person’s name and the necessary information.

Reinforcement Learning

The learning system is an agent in reinforcement learning. This agent will observe the environment and perform actions to receive rewards or penalties. It will learn by itself what’s the best strategy – called policy – to be rewarded more often. A policy defines what action the agent should choose in a given situation.

Reinforcement Learning is commonly used in robots with higher degrees of freedom, like walking, picking objects and opening doors!

Learning incrementally or not?

Batch Learning: the system doesn’t learn incrementally, so it must be trained using all available data at once, typically done offline. When the system is trained, it goes into action and doesn’t learn anymore. If the system needs to learn from new data, it must be stopped and replaced with a new system trained with the new data. As it might take a long time, batch learning systems can be automated and suited for dynamic use.

Online/Incremental Learning: the systems learn incrementally by feeding it continuous data instances (grouped in batches). Works great with data that changes a lot.

The Learning Rate is something that needs to be set when working with online learning systems. It’s a rate that defines how fast the algorithms should adapt to new data. Although a high learning rate will increase adaption to new data, the old data tends to be forgotten by the system. A learning rate with some inertia might be interesting to avoid data noise.

Instance-based or Model-based learning?

Generalization is an important task of machine learning systems. Algorithms must be able to generalize new instances, which means handling incoming data.

Instance-based learning: generalizes new data using a similarity measure. The system will compare incoming data with already-learned data and try to correctly assign new instances

Model-based learning: generalizes from a set of examples building a model from them. Such model will be used to predict where the incoming data will fit.

Summarizing

In order to build a machine learning system for your needs, the following points need to be specified:

How is it going to be trained? Supervised, Unsupervised, Semisupervised or through Reinforcement Learning?

How is it going to learn? Incrementally (online) or through batch learning (offline)?

How is it going to generalize? Instance or model based?

In Part 2, we’re going to build a machine learning system from scratch! Subscribe to my newsletter to keep updated.

In most cases, it’s not always the most popular person who gets the job done.

From all my experiences in the business world, meetings are (almost) always terrible. In the absence of leaders who would set things straight, meetings flow just as unmanned ships at the ocean.

Meetings have the obligation to be productive, otherwise, it’s simply a waste of time. Of course, that’s different than building a solid and healthy relationship with your co-workers or teammates. That’s extremely important, but business meetings must be designed to be productive and getting things done.

Do you even wonder why? Businesses are supposed to deliver value in the form of physical or digital products and services. Meetings are supposed to set and refresh operational points, data, and intelligence among leaders and workers – and that won’t get done by screwing around.

What is a business meeting?

A meeting is any encounter between two or more people to talk about anything.

A business meeting is an encounter between two or more people to talk about business perspective, progress update, feedback receival or any subject valuable and indispensable to operations.

Here’s a common scenario that we’ve all been through:

a meeting starts to talk about subject XYZ and, for the next thirty minutes, XYZ is not touched. Instead, participants engaged in what I call “ice-breaking conversation” – which is nothing but bullshit.

I’m like him in 97.492% of all meetings I attend

How to Handle Meetings

There are ways of making a meeting productive – if you’re an executive, that’s your obligation. Meetings must be work sessions, not bull sessions.

1. Decide what kind of meeting it will be

Different meetings require different types of preparation to have different results.

If there’s a meeting to write a marketing campaign, press release or something that needs to have a draft, a member or team has to prepare a draft beforehand. Otherwise, your meeting will be filled with brainstorms and conversation that won’t get the job done.

Objective meetings are supposed to ship the necessary/requested results at a glance. If you’re developing a new product, then you may arrange brainstorm/creative sessions, modularization, operations and scaling sessions.

If you’re dealing with a crisis, you may need results even faster. Delegating the right functions to the right teams will be a key to shipping such results.

Also, leaders can set meetings to happen in strategic parts of the day. Priorities should be handled early in the week – and that’s a nice excuse to arrange an 8 AM on Monday. Brainstorming or product development events may be handled after priorities are cleared.

Informal meetings, on the other hand, could be arranged

2. Reports

If one or all members report, the meeting should be confined to that matter.

Either there should be no discussion at all or the discussion should be limited to make the points clearer. If all reports must be discussed, then they should be previously emailed or handled to each member. Also, each report should have a predefined time-space.

3. Product Development

Product development and brainstorming sessions could be disastrous if there are no rules to be respected. Here are some points that might help you organize creative sessions:

defining the beginning and end of the meeting. If you planned a 1-hour session, such timeframe must be followed, especially if general thoughts are leading nowhere and except if thoughts and points are being extremely productive, then such meeting may be extended;

documenting valuable (and only valuable) points. These are the ideas and points that should be discussed or developed in next sessions or operation meetings;

don’t ask for unnecessary stuff. Just don’t.

4. Use your weapons

Slack, Google Drive, Dropbox, Evernote and thousands of other apps are there to make your day more productive. Stick to one or two platforms and integrate them as much as necessary – one of the things I offer in my consulting hours.

One of the biggest issues of my consulting clients who already post online content is focus. The goal is increasing customer attention and engaging in a further sale, but their original content ended up wasting their own time and guiding their customer nowhere.

It’s a mistake I’ve seen in all kinds of platforms – Facebook, Instagram, Email, YouTube…

If their lead clicked to see the content, he or she would just close the window or roll the screen after three seconds. You can imagine what kind of metrics such boring and annoying content produced – even with a considerable amount of access, qualitative metrics sucked.

Little or no customer retention, no further interest in other posts, no new subscriptions, unfollowing, and no sales. That’s all I have to deal while designing new strategies and funnels for clients.

Relieve yourself from pointless content

Designis where we should start while reviewing our content strategy. Relevance and design are fundamental traits a product must have to succeed – a brand may either supply an existing demand or create such demand.

In both cases, you need a decent marketing:

Customer discovery

Market research

Goals and metrics to follow

Channels to act

Languages to speak*

* Each platform has its very own language to diverse audiences. Think of a LinkedIn user and a Tumblr user.

If you’re selling an “intellectual property” product such as a book, service, courses, or even your personal brand, providing valuable content on the right channels is a must.

If your product is a fashion outfit or a movie, for instance, you might want to communicate more visually on Instagram or YouTube. Your value ladder will be established if your product seems good and your campaigns are persuasive enough.

In both cases, offering valuable or creative content may not be enough – that’s because people are extremely bored. By being bored and mentally tired, they might need a better and continuing approach to drive an action. I’ll talk about it in the next lines.

1. Know your Target Audience

This is always where to start when thinking about content strategy. Identifying your target audience must be a seriously made investigation, and also should be documented. Here’s what to look for:

Audience stereotype(s)

Platforms they are located (Instagram, YouTube, LinkedIn…)

How many hours they’re online on a daily basis

What kind of content they consume

Which websites they access

Such study should give us enough information to design our publication blueprints. Different products may have different audiences. A common mistake from retailers is to advertise all their products using a single strategy, or using little differentiation patterns such as gender only.

Advertising and selling apparel to a 35-year-old woman from Boston is different than advertising and selling to a teenager from California. Make sure to map your audience.

2. Set your objectives straight

Ideally, brands should design different posts for different campaigns. Imagine Hollister advertising their new summer collection – there’s a whole campaign behind each advertisement piece. This will avoid losing track of the right metrics and showing bullshit content to a specific targeted audience.

I started to realize that keeping a campaign with organic traffic, creative and interesting posts and improving everything analyzing our customer and metrics was not enough. And yes, that’s most of the story to get your campaign to be successful.

At the end of a busy day, while analyzing some average metrics from a campaign, I realized that every piece of ad could have cognitive elements to improve customer response. In my dictionary, customer responses are sales and brand advocacy. Period.

By cognitive elements, I mean everything that’s in the body of the post. Images, text, language, colors, call to action, duration, tones, and sounds if we’re talking about a video.

So I designed three Instagram posts which had “handmade” engagement logic. I emailed our designer to increase some color tones and change some backgrounds – it was a candy store from Dallas, so we were talking about chocolate, candies, and some colorful stuff.

After 24 hours, I had all I needed in hand: copywriting was ready, three images and one video ready to fly. I posted once a day, for three days, and then I would get back to the original campaign funnel.

Online orders boosted up to 25% more than the original campaign and in-store sales increased by 34% one day after the third post.

I saw myself obliged to redesign the whole campaign using cognitive elements – it has performed fantastically well both on paid ads to capture new followers and also for the organic traffic we had already reached.

3. Act Cognitively

Well, we all do it, right?

Cognitive Marketing is not your Holy Grail. No marketing, no book, no consultor, and no formula will be your Holy Grail.

David Ogilvy once taught us that great marketing will help you sell your product – but just once. If people are disappointed and no improvements are made, they won’t buy products from you again. Also, if unhappy customers are left behind with no support, it will be even harder.

Cognitive elements will increase actions from your campaigns. It could be a subscription to your YouTube channel, following your Instagram or Facebook account, buying a product or recommending your brand to someone.

Functions are coded to perform actions and manipulate variables. They are part of a core component in programming. In Swift, functions are very simple to write and, if you have a basis of Object Oriented Programming, you can reuse them throughout your program.

Functions are used in all sorts of programs. You can write a function to calculate the number of calories you ate this morning (I ate a lot, btw), how many miles you should run to burn them or even if you can purchase a book using your credit card!

Welcome!

This post will serve as an intro and reference guide to all Swift posts and tutorials which I’ve been working on to post on my blog. Feel free to comment your thoughts and feedback in this section and all posts from me you come across.

I will keep this section updated on a logical path for you to get the most of this great language!

Ready to Swiftify?

First of all, we must define what a derivative is. It is simply a security whose price relies upon another asset – called an underlying asset. An S&P future and an American call option are derivatives.

A security is a non-negotiable financial instrument with monetary value – you may sell it for cash. It could be the minimum part of a company (a stock) or one bushel (60 pounds) of soybeans.

The internet and social media set new standards in the whole commercial process. People are more likely to buy from people or brands they’ve known for a while in social media, which gave them steady relevant content.

Most entrepreneurs (or fakepreneurs) still don’t know what’s really going on in marketing nowadays. Those who claim to be “experts” in digital marketing don’t even know what real marketing is about, especially in the 21st century.

The truth is: bad players will be thrown away. What do I mean by that?

Bad communicators won’t sell their products

New products by unknown brands will struggle to reach their audience and sell

Billion-dollar brands will disappear because they’re ignoring this movement

Fakepreneurs will continue to rise, and then disappear

Millions of dollars will still be burned in wrong advertising, then comes bankruptcy

Relevance is always king

No doubt all that came with the internet and the mobile world. All sorts of businesses should constantly be reviewing their marketing and communication strategies to satisfy their costumer.

The customer is eager and knows what to buy

This perhaps hasn’t been and won’t be a universal truth, but that’s how you should be handling your marketing.

Especially if you’re new to the market and won’t be burning millions into paid advertising, you should be offering high relevance content to your leads and people who follow you on social media. It’s the type of content that will make them stop scrolling their feed to read, watch and pay attention.

The Brazilian house of representatives archived on August 2, 2017, an investigation that could possibly impeach President Temer. The main accusation is passive corruption in a scandal involving Joesley Batista, CEO of the JBS conglomerate. For a matter of fact, many brands that Brazilians consume daily are part of Joesley’s conglomerate.

Let’s take a quick look in Brazil:

Former President Dilma was impeached in 2016;

Actual President Temer could be investigated and impeached;

President of the house of representatives – Rodrigo Maia, who would become President if Temer was impeached – could also be investigated and impeached;

There are almost 70,000 homicides per year in the country – and nobody talks about it;

15 million people are unemployed.

Hopefully, the economy started to react after important decisions made by Temer administration. Inflation was reduced from 11% to 4%, federal expenses were legally reduced for the next 25 years and Real valorized.